HAResformer: A Hybrid ResNet-Transformer Hierarchical Aggregation Architecture for Visible-Infrared Person Reidentification

• Published in: IEEE internet of things journal Vol. 12; no. 12; pp. 21691 - 21703
• Main Authors: Qian, Yongheng, Tang, Su-Kit
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 15.06.2025; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Aggregates; Artificial neural networks; Coders; Computer vision; Convolutional neural networks; Convolutional neural networks (CNN); cross-modality; Data mining; Feature extraction; feature fusion; Identification of persons; Internet of Things; multiscale supervision; person reidentification (ReID); Representations; Robustness; Semantics; Spatial data; Transformers; vision transformer; Visualization
• ISSN: 2327-4662; 2327-4662
• DOI: 10.1109/JIOT.2025.3547920

Modality differences and intramodality variations make the visible-infrared person reidentification (VI-ReID) task highly challenging. Most existing methods focus on building network frameworks based on convolutional neural networks (CNN) or pure vision transformers (ViT) to extract discriminative features and address these challenges. However, these methods neglect several key issues: deeply fusing local features with global spatial information enhances comprehensive discriminative representation, patch tokens contain rich semantic information, and different feature extraction stages within the network emphasize various semantic elements. To address these issues, we propose a novel hybrid ResNet-transformer hierarchical aggregation architecture named HAResformer. HAResformer comprises three key components: 1) hierarchical feature extraction (HFE) framework; 2) deeply supervised aggregation (DSA); and 3) hierarchical global aggregate encoder (HGAE). Specifically, HFE introduces a lightweight cross-encoder feature fusion module (CFFM) to deeply integrate the local features and global spatial information of a person extracted by the ResNet encoder (RE) and transformer encoder (TE). Subsequently, the fused features are fed as global priors into the next-stage TE for deep interaction, aiming to extract specific local features and global contextual clues. Additionally, DSA and HGAE provide auxiliary supervision and aggregation on multiscale features to enhance multigranularity feature representation. HAResformer effectively alleviates modality differences and reduces intramodality variations. Extensive experiments on three benchmarks demonstrate the effectiveness and generalization of our architecture and outperform most state-of-the-art methods. HAResformer has the potential to become a new VI-ReID baseline, promoting high-quality research in the future.